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Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework

Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework

Cristhian Figueroa, Iacopo Vagliano, Oscar Rodríguez Rocha, Marco Torchiano, Catherine Faron Zucker, Juan Carlos Corrales, Maurizio Morisio
Copyright: © 2019 |Pages: 30
ISBN13: 9781522571865|ISBN10: 1522571868|ISBN13 Softcover: 9781522587231|EISBN13: 9781522571872
DOI: 10.4018/978-1-5225-7186-5.ch002
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MLA

Figueroa, Cristhian, et al. "Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework." Semantic Web Science and Real-World Applications, edited by Miltiadis D. Lytras, et al., IGI Global, 2019, pp. 18-47. https://doi.org/10.4018/978-1-5225-7186-5.ch002

APA

Figueroa, C., Vagliano, I., Rocha, O. R., Torchiano, M., Zucker, C. F., Corrales, J. C., & Morisio, M. (2019). Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework. In M. Lytras, N. Aljohani, E. Damiani, & K. Chui (Eds.), Semantic Web Science and Real-World Applications (pp. 18-47). IGI Global. https://doi.org/10.4018/978-1-5225-7186-5.ch002

Chicago

Figueroa, Cristhian, et al. "Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework." In Semantic Web Science and Real-World Applications, edited by Miltiadis D. Lytras, et al., 18-47. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7186-5.ch002

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Abstract

Data published on the web following the principles of linked data has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use linked data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for linked data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset.

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